Suppr超能文献

网络指标对链接错误的稳健性。

Robustness of network measures to link errors.

作者信息

Platig J, Ott E, Girvan M

机构信息

Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, USA and Metabolism Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.

Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Dec;88(6):062812. doi: 10.1103/PhysRevE.88.062812. Epub 2013 Dec 11.

Abstract

In various applications involving complex networks, network measures are employed to assess the relative importance of network nodes. However, the robustness of such measures in the presence of link inaccuracies has not been well characterized. Here we present two simple stochastic models of false and missing links and study the effect of link errors on three commonly used node centrality measures: degree centrality, betweenness centrality, and dynamical importance. We perform numerical simulations to assess robustness of these three centrality measures. We also develop an analytical theory, which we compare with our simulations, obtaining very good agreement.

摘要

在各种涉及复杂网络的应用中,网络测度被用于评估网络节点的相对重要性。然而,在存在链路不准确的情况下,此类测度的稳健性尚未得到很好的刻画。在此,我们提出了两种关于错误链路和缺失链路的简单随机模型,并研究链路误差对三种常用节点中心性测度的影响:度中心性、介数中心性和动态重要性。我们进行数值模拟以评估这三种中心性测度的稳健性。我们还发展了一种解析理论,并将其与我们的模拟结果进行比较,得到了非常好的一致性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验